CLUSTER MAPPING OF HOTSPOTS USING KERNEL DENSITY ESTIMATION IN WEST KALIMANTAN
Abstract
Forest and land fires pose a recurring concern every year in Indonesia, often taking place in West Kalimantan Province, particularly during the dry season. This study aims to use the Kernel Density Estimation (KDE) to categorize the data of the hotspots in the province of West Kalimantan according to their density and to map the cluster level of the fire risks in the region. The data utilized in this study are secondary data obtained from the images of the MODIS (Moderate Resolution Imaging Spectroradiometer) instrument, which are available on firms.modaps.eosdis.nasa.gov and provided by NASA. The data focuses on hotspots dispersed across West Kalimantan province during 2020. The variables examined in the study were the confidence level (≥80%) of forest and land fire hotspots, the distance from each point to the nearest river, and the distance from each point to the nearest road. The kernel density estimation method with a Gaussian kernel function yielded clustering results into three distinct groups according to their vulnerability levels. Low vulnerability areas comprise Cluster 1, which consists of 127 points or 50.97% of the total hotspots. Medium vulnerability areas belong to Cluster 2, which has 47 points or 30.32% of the total. Cluster 3 includes high vulnerability locations, consisting of 29 points or 18.71% of the total. The most susceptible areas to forest and land fires are located within the Ketapang regency.
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